School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Bioinformatics. 2024 Oct 1;40(10). doi: 10.1093/bioinformatics/btae570.
The prediction of drug-target interaction is a vital task in the biomedical field, aiding in the discovery of potential molecular targets of drugs and the development of targeted therapy methods with higher efficacy and fewer side effects. Although there are various methods for drug-target interaction (DTI) prediction based on heterogeneous information networks, these methods face challenges in capturing the fundamental interaction between drugs and targets and ensuring the interpretability of the model. Moreover, they need to construct meta-paths artificially or a lot of feature engineering (prior knowledge), and graph generation can fuse information more flexibly without meta-path selection.
We propose a causal enhanced method for drug-target interaction (CE-DTI) prediction that integrates graph generation and multi-source information fusion. First, we represent drugs and targets by modeling the fusion of their multi-source information through automatic graph generation. Once drugs and targets are combined, a network of drug-target pairs is constructed, transforming the prediction of drug-target interactions into a node classification problem. Specifically, the influence of surrounding nodes on the central node is separated into two groups: causal and non-causal variable nodes. Causal variable nodes significantly impact the central node's classification, while non-causal variable nodes do not. Causal invariance is then used to enhance the contrastive learning of the drug-target pairs network. Our method demonstrates excellent performance compared with other competitive benchmark methods across multiple datasets. At the same time, the experimental results also show that the causal enhancement strategy can explore the potential causal effects between DTPs, and discover new potential targets. Additionally, case studies demonstrate that this method can identify potential drug targets.
The source code of AdaDR is available at: https://github.com/catly/CE-DTI.
药物-靶标相互作用的预测是生物医学领域的一项重要任务，有助于发现药物的潜在分子靶标，并开发具有更高疗效和更少副作用的靶向治疗方法。尽管基于异构信息网络的药物-靶标相互作用（DTI）预测有各种方法，但这些方法在捕捉药物和靶标之间的基本相互作用以及确保模型的可解释性方面面临挑战。此外，它们需要人工构建元路径或进行大量的特征工程（先验知识），而图生成可以更灵活地融合信息，无需元路径选择。
我们提出了一种因果增强的药物-靶标相互作用预测方法（CE-DTI），该方法集成了图生成和多源信息融合。首先，我们通过通过自动图生成来模拟融合它们的多源信息来表示药物和靶标。一旦药物和靶标结合，就构建了一个药物-靶标对网络，将药物-靶标相互作用的预测转化为节点分类问题。具体来说，将周围节点对中心节点的影响分为两组：因果变量节点和非因果变量节点。因果变量节点对中心节点的分类有显著影响，而非因果变量节点则没有。然后使用因果不变性来增强药物-靶标对网络的对比学习。我们的方法在多个数据集上与其他竞争基准方法相比表现出色。同时，实验结果还表明，因果增强策略可以探索 DTP 之间的潜在因果效应，并发现新的潜在靶标。此外，案例研究表明，该方法可以识别潜在的药物靶标。
AdaDR 的源代码可在 https://github.com/catly/CE-DTI 获得。
